How Long to Read Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles

How Long Does it Take to Read Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles?

It takes the average reader 2 hours and 4 minutes to read Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles by Li Yeuching

Assuming a reading speed of 250 words per minute. Learn more

Description

The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not only being capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller.

How long is Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles?

Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles by Li Yeuching is 123 pages long, and a total of 31,119 words.

This makes it 42% the length of the average book. It also has 38% more words than the average book.

How Long Does it Take to Read Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles Aloud?

The average oral reading speed is 183 words per minute. This means it takes 2 hours and 50 minutes to read Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles aloud.

What Reading Level is Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles?

Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles is suitable for students ages 10 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

Where Can I Buy Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles?

Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles by Li Yeuching is sold by several retailers and bookshops. However, Read Time works with Amazon to provide an easier way to purchase books.

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